We compare our results to the state of art method CP-VTON [1]}. Since the code for CP-VTON is publicly released [2]}, we are able to obtain results on our dataset for comparison.
The state of the art method MG-VTON [3]} tries to solve the problem in a significantly different way than ours, but unfortunately their code is not available. Therefore, our comparison was limited to CP-VTON.
We perform our evaluation on test data that consist of 900 paired images.
The results are shown in Table REF . SSIM and IS scores are included for the final stage of Poly-GAN, as well as for Stages 2 and 3 (after stitching the head of the model). The results illustrate that Poly-GAN outperforms CP-VTON in all cases. By comparing the scores of different stages, we see that the output of Stage 2 is the sharpest and has the highest IS score, but it suffers from holes due to blank regions. The output of Stage 3 has the highest SSIM score, i.e. is the most similar to the desired output, but it suffers from blurriness artifacts. The combination on the two gives a balanced score without scoring the highest for either metric.
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